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A probabilistic model for forging flaw crack nucleation processes

: Radaelli, F.; Amann, C.; Aydin, A.; Varfolomeev, I.; Gumbsch, P.; Kadau, K.


American Society of Mechanical Engineers -ASME-; International Gas Turbine Institute:
ASME Turbo Expo 2020. Turbomachinery Technical Conference and Exposition. Vol.10B: Structures and Dynamics. Proceedings : Presented at the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, September 21-25, 2020, online
New York/NY.: ASME, 2020
ISBN: 978-0-7918-8422-5
Paper GT2020-14606, 10 S.
Turbomachinery Technical Conference and Exposition (Turbo Expo) <2020, Online>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
AG Turbo Grant; 03ET7091G
Fraunhofer IWM ()
crack nucleation; LCF; probabilistic; rotor

A probabilistic model for quantifying the number of load cycles for nucleation of forging flaws into a crack has been developed. The model correlates low cycle fatigue (LCF) data, ultrasonic testing (UT) indication data, flaw morphology and type with the nucleation process. The nucleation model is based on a probabilistic LCF model applied to finite element analyses (FEA) of flaw geometries. The model includes statistical size and notch effects. In order to calibrate the model, we conducted experiments involving specimens that include forging flaws. The specimens were machined out from heavy duty steel rotor disks for the energy sector. The large disks, including ultrasonic indications on the millimeter scale, were cut into smaller segments in order to efficiently machine specimens includi ng manufacturing related forging flaws. We conducted cyclic loading experiments at a variety of temperatures and high stresses in order to capture realistic engine operating conditions for flaws as they occur in service. This newly developed model can be incorporated into an existing probabilistic fracture mechanics framework and enables a reliable risk quantification allowing to support customer needs for more flexible operational profiles due to the emergence of renewable energy sources.